scikit-learn
diff --git a/‎sklearn/impute/_base.py‎
Lines changed: 13 additions & 2 deletions b/‎sklearn/impute/_base.py‎
Lines changed: 13 additions & 2 deletions
@@ -136,7 +136,10 @@ def _more_tags(self):
 
 
 class SimpleImputer(_BaseImputer):
-    """Imputation transformer for completing missing values.
+    """Univariate imputer for completing missing values with simple strategies.
+
+    Replace missing values using a descriptive statistic (e.g. mean, median, or
+    most frequent) along each column, or using a constant value.
 
     Read more in the :ref:`User Guide <impute>`.
 
@@ -224,13 +227,21 @@ class SimpleImputer(_BaseImputer):
 
     See Also
     --------
-    IterativeImputer : Multivariate imputation of missing values.
+    IterativeImputer : Multivariate imputer that estimates values to impute for
+        each feature with missing values from all the others.
+    KNNImputer : Multivariate imputer that estimates missing features using
+        nearest samples.
 
     Notes
     -----
     Columns which only contained missing values at :meth:`fit` are discarded
     upon :meth:`transform` if strategy is not `"constant"`.
 
+    In a prediction context, simple imputation usually performs poorly when
+    associated with a weak learner. However, with a powerful learner, it can
+    lead to as good or better performance than complex imputation such as
+    :class:`~sklearn.impute.IterativeImputer` or :class:`~sklearn.impute.KNNImputer`.
+
     Examples
     --------
     >>> import numpy as np