diff --git a/docs/source/release_notes.rst b/docs/source/release_notes.rst
index 14d30a0f37..45569b51df 100644
--- a/docs/source/release_notes.rst
+++ b/docs/source/release_notes.rst
@@ -11,6 +11,7 @@ Release Notes
     * Fixes
         * Updated TextFeaturizer component to no longer require an internet connection to run :pr:`1022`
         * Fixed non-deterministic element of TextFeaturizer transformations :pr:`1022`
+        * Added a StandardScaler to all ElasticNet pipelines :pr:`1065`
     * Changes
         * Added `needs_fitting` property to ComponentBase :pr:`1044`
         * Updated references to data types to use datatype lists defined in `evalml.utils.gen_utils` :pr:`1039`
diff --git a/evalml/pipelines/utils.py b/evalml/pipelines/utils.py
index d3522dc104..f73ada3fca 100644
--- a/evalml/pipelines/utils.py
+++ b/evalml/pipelines/utils.py
@@ -6,14 +6,13 @@
 )
 from .regression_pipeline import RegressionPipeline
 
+from evalml.model_family import ModelFamily
 from evalml.pipelines.components import (
     CatBoostClassifier,
     CatBoostRegressor,
     DateTimeFeaturizer,
     DropNullColumns,
     Imputer,
-    LinearRegressor,
-    LogisticRegressionClassifier,
     OneHotEncoder,
     StandardScaler
 )
@@ -56,7 +55,7 @@ def _get_preprocessing_components(X, y, problem_type, estimator_class):
     if (add_datetime_featurizer or len(categorical_cols.columns) > 0) and estimator_class not in {CatBoostClassifier, CatBoostRegressor}:
         pp_components.append(OneHotEncoder)
 
-    if estimator_class in {LinearRegressor, LogisticRegressionClassifier}:
+    if estimator_class.model_family == ModelFamily.LINEAR_MODEL:
         pp_components.append(StandardScaler)
     return pp_components
 
diff --git a/evalml/tests/pipeline_tests/test_pipelines.py b/evalml/tests/pipeline_tests/test_pipelines.py
index 316c42a4f0..c86436ba8d 100644
--- a/evalml/tests/pipeline_tests/test_pipelines.py
+++ b/evalml/tests/pipeline_tests/test_pipelines.py
@@ -25,6 +25,8 @@
 from evalml.pipelines.components import (
     DateTimeFeaturizer,
     DropNullColumns,
+    ElasticNetClassifier,
+    ElasticNetRegressor,
     Imputer,
     LinearRegressor,
     LogisticRegressionClassifier,
@@ -114,6 +116,11 @@ def test_make_pipeline():
     assert isinstance(binary_pipeline, type(BinaryClassificationPipeline))
     assert binary_pipeline.component_graph == [DropNullColumns, Imputer, DateTimeFeaturizer, OneHotEncoder, StandardScaler, LogisticRegressionClassifier]
 
+    en_binary_pipeline = make_pipeline(X, y, ElasticNetClassifier, ProblemTypes.BINARY)
+    assert isinstance(en_binary_pipeline, type(BinaryClassificationPipeline))
+    assert en_binary_pipeline.component_graph == [DropNullColumns, Imputer, DateTimeFeaturizer, OneHotEncoder,
+                                                  StandardScaler, ElasticNetClassifier]
+
     binary_pipeline = make_pipeline(X, y, RandomForestClassifier, ProblemTypes.BINARY)
     assert isinstance(binary_pipeline, type(BinaryClassificationPipeline))
     assert binary_pipeline.component_graph == [DropNullColumns, Imputer, DateTimeFeaturizer, OneHotEncoder, RandomForestClassifier]
@@ -123,6 +130,11 @@ def test_make_pipeline():
     assert isinstance(multiclass_pipeline, type(MulticlassClassificationPipeline))
     assert multiclass_pipeline.component_graph == [DropNullColumns, Imputer, DateTimeFeaturizer, OneHotEncoder, StandardScaler, LogisticRegressionClassifier]
 
+    en_multiclass_pipeline = make_pipeline(X, y, ElasticNetClassifier, ProblemTypes.MULTICLASS)
+    assert isinstance(en_multiclass_pipeline, type(MulticlassClassificationPipeline))
+    assert en_multiclass_pipeline.component_graph == [DropNullColumns, Imputer, DateTimeFeaturizer, OneHotEncoder,
+                                                      StandardScaler, ElasticNetClassifier]
+
     regression_pipeline = make_pipeline(X, y, RandomForestRegressor, ProblemTypes.REGRESSION)
     assert isinstance(regression_pipeline, type(RegressionPipeline))
     assert regression_pipeline.component_graph == [DropNullColumns, Imputer, DateTimeFeaturizer, OneHotEncoder, RandomForestRegressor]
@@ -131,6 +143,11 @@ def test_make_pipeline():
     assert isinstance(regression_pipeline, type(RegressionPipeline))
     assert regression_pipeline.component_graph == [DropNullColumns, Imputer, DateTimeFeaturizer, OneHotEncoder, StandardScaler, LinearRegressor]
 
+    en_multiclass_pipeline = make_pipeline(X, y, ElasticNetRegressor, ProblemTypes.REGRESSION)
+    assert isinstance(en_multiclass_pipeline, type(RegressionPipeline))
+    assert en_multiclass_pipeline.component_graph == [DropNullColumns, Imputer, DateTimeFeaturizer, OneHotEncoder,
+                                                      StandardScaler, ElasticNetRegressor]
+
 
 def test_make_pipeline_no_nulls():
     X = pd.DataFrame({"numerical": [1, 2, 3, 1, 2],