diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 43078ef3a263c..aea7a2231582a 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -8156,7 +8156,6 @@ def asof(self, where, subset=None):
     # ----------------------------------------------------------------------
     # Action Methods
 
-    @doc(klass=_shared_doc_kwargs["klass"])
     def isna(self) -> Self:
         """
         Detect missing values.
@@ -8169,15 +8168,18 @@ def isna(self) -> Self:
 
         Returns
         -------
-        {klass}
-            Mask of bool values for each element in {klass} that
-            indicates whether an element is an NA value.
+        Series/DataFrame
+            Mask of bool values for each element in Series/DataFrame
+            that indicates whether an element is an NA value.
 
         See Also
         --------
-        {klass}.isnull : Alias of isna.
-        {klass}.notna : Boolean inverse of isna.
-        {klass}.dropna : Omit axes labels with missing values.
+        Series.isnull : Alias of isna.
+        DataFrame.isnull : Alias of isna.
+        Series.notna : Boolean inverse of isna.
+        DataFrame.notna : Boolean inverse of isna.
+        Series.dropna : Omit axes labels with missing values.
+        DataFrame.dropna : Omit axes labels with missing values.
         isna : Top-level isna.
 
         Examples
@@ -8225,11 +8227,77 @@ def isna(self) -> Self:
         """
         return isna(self).__finalize__(self, method="isna")
 
-    @doc(isna, klass=_shared_doc_kwargs["klass"])
     def isnull(self) -> Self:
+        """
+        Detect missing values.
+
+        Return a boolean same-sized object indicating if the values are NA.
+        NA values, such as None or :attr:`numpy.NaN`, gets mapped to True
+        values.
+        Everything else gets mapped to False values. Characters such as empty
+        strings ``''`` or :attr:`numpy.inf` are not considered NA values.
+
+        Returns
+        -------
+        Series/DataFrame
+            Mask of bool values for each element in Series/DataFrame
+            that indicates whether an element is an NA value.
+
+        See Also
+        --------
+        Series.isna : Alias of isnull.
+        DataFrame.isna : Alias of isnull.
+        Series.notna : Boolean inverse of isnull.
+        DataFrame.notna : Boolean inverse of isnull.
+        Series.dropna : Omit axes labels with missing values.
+        DataFrame.dropna : Omit axes labels with missing values.
+        isna : Top-level isna.
+
+        Examples
+        --------
+        Show which entries in a DataFrame are NA.
+
+        >>> df = pd.DataFrame(
+        ...     dict(
+        ...         age=[5, 6, np.nan],
+        ...         born=[
+        ...             pd.NaT,
+        ...             pd.Timestamp("1939-05-27"),
+        ...             pd.Timestamp("1940-04-25"),
+        ...         ],
+        ...         name=["Alfred", "Batman", ""],
+        ...         toy=[None, "Batmobile", "Joker"],
+        ...     )
+        ... )
+        >>> df
+           age       born    name        toy
+        0  5.0        NaT  Alfred        NaN
+        1  6.0 1939-05-27  Batman  Batmobile
+        2  NaN 1940-04-25              Joker
+
+        >>> df.isna()
+             age   born   name    toy
+        0  False   True  False   True
+        1  False  False  False  False
+        2   True  False  False  False
+
+        Show which entries in a Series are NA.
+
+        >>> ser = pd.Series([5, 6, np.nan])
+        >>> ser
+        0    5.0
+        1    6.0
+        2    NaN
+        dtype: float64
+
+        >>> ser.isna()
+        0    False
+        1    False
+        2     True
+        dtype: bool
+        """
         return isna(self).__finalize__(self, method="isnull")
 
-    @doc(klass=_shared_doc_kwargs["klass"])
     def notna(self) -> Self:
         """
         Detect existing (non-missing) values.
@@ -8242,15 +8310,18 @@ def notna(self) -> Self:
 
         Returns
         -------
-        {klass}
-            Mask of bool values for each element in {klass} that
-            indicates whether an element is not an NA value.
+        Series/DataFrame
+            Mask of bool values for each element in Series/DataFrame
+            that indicates whether an element is not an NA value.
 
         See Also
         --------
-        {klass}.notnull : Alias of notna.
-        {klass}.isna : Boolean inverse of notna.
-        {klass}.dropna : Omit axes labels with missing values.
+        Series.notnull : Alias of notna.
+        DataFrame.notnull : Alias of notna.
+        Series.isna : Boolean inverse of notna.
+        DataFrame.isna : Boolean inverse of notna.
+        Series.dropna : Omit axes labels with missing values.
+        DataFrame.dropna : Omit axes labels with missing values.
         notna : Top-level notna.
 
         Examples
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 1f027b722b3d9..fe71a3ab91933 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -6183,7 +6183,7 @@ def isna(self) -> Series:
         return NDFrame.isna(self)
 
     # error: Cannot determine type of 'isna'
-    @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])  # type: ignore[has-type]
+    @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])
     def isnull(self) -> Series:
         """
         Series.isnull is an alias for Series.isna.
@@ -6260,7 +6260,7 @@ def notna(self) -> Series:
         return super().notna()
 
     # error: Cannot determine type of 'notna'
-    @doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"])  # type: ignore[has-type]
+    @doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"])
     def notnull(self) -> Series:
         """
         Series.notnull is an alias for Series.notna.