diff --git a/sklearn/preprocessing/data.py b/sklearn/preprocessing/data.py
index e7f242cdedc5d..08be1c75d0f49 100644
--- a/sklearn/preprocessing/data.py
+++ b/sklearn/preprocessing/data.py
@@ -1737,6 +1737,9 @@ class OneHotEncoder(BaseEstimator, TransformerMixin):
     This encoding is needed for feeding categorical data to many scikit-learn
     estimators, notably linear models and SVMs with the standard kernels.
 
+    Note: a one-hot encoding of y labels should use a LabelBinarizer
+    instead.
+
     Read more in the :ref:`User Guide <preprocessing_categorical_features>`.
 
     Parameters
@@ -1810,6 +1813,13 @@ class OneHotEncoder(BaseEstimator, TransformerMixin):
       dictionary items (also handles string-valued features).
     sklearn.feature_extraction.FeatureHasher : performs an approximate one-hot
       encoding of dictionary items or strings.
+    sklearn.preprocessing.LabelBinarizer : binarizes labels in a one-vs-all
+      fashion.
+    sklearn.preprocessing.MultiLabelBinarizer : transforms between iterable of
+      iterables and a multilabel format, e.g. a (samples x classes) binary
+      matrix indicating the presence of a class label.
+    sklearn.preprocessing.LabelEncoder : encodes labels with values between 0
+      and n_classes-1.
     """
     def __init__(self, n_values="auto", categorical_features="all",
                  dtype=np.float64, sparse=True, handle_unknown='error'):
diff --git a/sklearn/preprocessing/label.py b/sklearn/preprocessing/label.py
index e571d3f44be7f..7a391b3f60b19 100644
--- a/sklearn/preprocessing/label.py
+++ b/sklearn/preprocessing/label.py
@@ -91,6 +91,10 @@ class LabelEncoder(BaseEstimator, TransformerMixin):
     >>> list(le.inverse_transform([2, 2, 1]))
     ['tokyo', 'tokyo', 'paris']
 
+    See also
+    --------
+    sklearn.preprocessing.OneHotEncoder : encode categorical integer features
+        using a one-hot aka one-of-K scheme.
     """
 
     def fit(self, y):
@@ -257,6 +261,8 @@ class LabelBinarizer(BaseEstimator, TransformerMixin):
     --------
     label_binarize : function to perform the transform operation of
         LabelBinarizer with fixed classes.
+    sklearn.preprocessing.OneHotEncoder : encode categorical integer features
+        using a one-hot aka one-of-K scheme.
     """
 
     def __init__(self, neg_label=0, pos_label=1, sparse_output=False):
@@ -648,6 +654,7 @@ class MultiLabelBinarizer(BaseEstimator, TransformerMixin):
 
     Examples
     --------
+    >>> from sklearn.preprocessing import MultiLabelBinarizer
     >>> mlb = MultiLabelBinarizer()
     >>> mlb.fit_transform([(1, 2), (3,)])
     array([[1, 1, 0],
@@ -661,6 +668,10 @@ class MultiLabelBinarizer(BaseEstimator, TransformerMixin):
     >>> list(mlb.classes_)
     ['comedy', 'sci-fi', 'thriller']
 
+    See also
+    --------
+    sklearn.preprocessing.OneHotEncoder : encode categorical integer features
+        using a one-hot aka one-of-K scheme.
     """
     def __init__(self, classes=None, sparse_output=False):
         self.classes = classes