diff --git a/doc/modules/tree.rst b/doc/modules/tree.rst
index f793c34b7f53d..3f577795e24be 100644
--- a/doc/modules/tree.rst
+++ b/doc/modules/tree.rst
@@ -481,7 +481,10 @@ Regression criteria
 
 If the target is a continuous value, then for node :math:`m`,
 representing a region :math:`R_m` with :math:`N_m` observations, common
-criteria to minimise are
+criteria to minimise as for determining locations for future
+splits are Mean Squared Error, which minimizes the L2 error
+using mean values at terminal nodes, and Mean Absolute Error, which 
+minimizes the L1 error using median values at terminal nodes. 
 
 Mean Squared Error:
 
diff --git a/sklearn/tree/tree.py b/sklearn/tree/tree.py
index 93db4eb98f34e..099f3da39a45b 100644
--- a/sklearn/tree/tree.py
+++ b/sklearn/tree/tree.py
@@ -879,8 +879,11 @@ class DecisionTreeRegressor(BaseDecisionTree, RegressorMixin):
     criterion : string, optional (default="mse")
         The function to measure the quality of a split. Supported criteria
         are "mse" for the mean squared error, which is equal to variance
-        reduction as feature selection criterion, and "mae" for the mean
-        absolute error.
+        reduction as feature selection criterion and minimizes the L2 loss
+        using the mean of each terminal node, "friedman_mse", which uses mean
+        squared error with Friedman's improvement score for potential splits,
+        and "mae" for the mean absolute error, which minimizes the L1 loss
+        using the median of each terminal node.
 
         .. versionadded:: 0.18
            Mean Absolute Error (MAE) criterion.