diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index 3804f5c01c26c..13b1143215027 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -69,6 +69,11 @@ Enhancements
      (`#7723 <https://github.com/scikit-learn/scikit-learn/pull/7723>`_)
      by `Mikhail Korobov`_.
 
+   - Added ``flatten_transform`` parameter to :class:`ensemble.VotingClassifier`
+     to change output shape of `transform` method to 2 dimensional.
+     (`#7794 <https://github.com/scikit-learn/scikit-learn/pull/7794>`_)
+     by `Ibraim Ganiev`_.
+
 Bug fixes
 .........
 
diff --git a/sklearn/ensemble/tests/test_voting_classifier.py b/sklearn/ensemble/tests/test_voting_classifier.py
index f3a5274ab3f4c..6268426b0eeae 100644
--- a/sklearn/ensemble/tests/test_voting_classifier.py
+++ b/sklearn/ensemble/tests/test_voting_classifier.py
@@ -210,7 +210,7 @@ def test_gridsearch():
     grid.fit(iris.data, iris.target)
 
 
-def test_parallel_predict():
+def test_parallel_fit():
     """Check parallel backend of VotingClassifier on toy dataset."""
     clf1 = LogisticRegression(random_state=123)
     clf2 = RandomForestClassifier(random_state=123)
@@ -258,3 +258,23 @@ def test_sample_weight():
         voting='soft')
     msg = ('Underlying estimator \'knn\' does not support sample weights.')
     assert_raise_message(ValueError, msg, eclf3.fit, X, y, sample_weight)
+
+
+def test_transform():
+    """Check trqansform method of VotingClassifier on toy dataset."""
+    clf1 = LogisticRegression(random_state=123)
+    clf2 = RandomForestClassifier(random_state=123)
+    clf3 = GaussianNB()
+    X = np.array([[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])
+    y = np.array([1, 1, 2, 2])
+
+    eclf1 = VotingClassifier(estimators=[
+        ('lr', clf1), ('rf', clf2), ('gnb', clf3)],
+        voting='soft').fit(X, y)
+    eclf2 = VotingClassifier(estimators=[
+        ('lr', clf1), ('rf', clf2), ('gnb', clf3)],
+        voting='soft',
+        flatten_transform=True).fit(X, y)
+
+    assert_array_equal(eclf1.transform(X).shape, (3, 4, 2))
+    assert_array_equal(eclf2.transform(X).shape, (4, 6))
diff --git a/sklearn/ensemble/voting_classifier.py b/sklearn/ensemble/voting_classifier.py
index 40e21d56270db..a77b24c9baf04 100644
--- a/sklearn/ensemble/voting_classifier.py
+++ b/sklearn/ensemble/voting_classifier.py
@@ -12,6 +12,7 @@
 # License: BSD 3 clause
 
 import numpy as np
+import warnings
 
 from ..base import BaseEstimator
 from ..base import ClassifierMixin
@@ -61,6 +62,12 @@ class VotingClassifier(BaseEstimator, ClassifierMixin, TransformerMixin):
         The number of jobs to run in parallel for ``fit``.
         If -1, then the number of jobs is set to the number of cores.
 
+    flatten_transform : bool, optional (default=False)
+        Affects shape of transform output only when voting='soft'
+        If voting='soft' and flatten_transform=True, transform method returns
+        matrix with shape [n_samples, n_classifiers * n_classes] instead of
+        [n_classifiers, n_samples, n_classes].
+
     Attributes
     ----------
     estimators_ : list of classifiers
@@ -100,12 +107,14 @@ class VotingClassifier(BaseEstimator, ClassifierMixin, TransformerMixin):
     >>>
     """
 
-    def __init__(self, estimators, voting='hard', weights=None, n_jobs=1):
+    def __init__(self, estimators, voting='hard', weights=None, n_jobs=1,
+                 flatten_transform=False):
         self.estimators = estimators
         self.named_estimators = dict(estimators)
         self.voting = voting
         self.weights = weights
         self.n_jobs = n_jobs
+        self.flatten_transform = flatten_transform
 
     def fit(self, X, y, sample_weight=None):
         """ Fit the estimators.
@@ -238,16 +247,25 @@ def transform(self, X):
 
         Returns
         -------
-        If `voting='soft'`:
+        If `voting='soft'` and `flatten_transform=False`:
           array-like = [n_classifiers, n_samples, n_classes]
             Class probabilities calculated by each classifier.
+        If `voting='soft'` and `flatten_transform=True`:
+          array-like = [n_samples, n_classifiers * n_classes]
+            Class probabilities calculated by each classifier.
         If `voting='hard'`:
           array-like = [n_samples, n_classifiers]
             Class labels predicted by each classifier.
         """
         check_is_fitted(self, 'estimators_')
         if self.voting == 'soft':
-            return self._collect_probas(X)
+            probas = self._collect_probas(X)
+            if not self.flatten_transform:
+                warnings.warn("'flatten_transform' default value will be"
+                              " changed to True in 0.21.", DeprecationWarning)
+                return probas
+            else:
+                return np.hstack(probas)
         else:
             return self._predict(X)