diff --git a/doc/modules/grid_search.rst b/doc/modules/grid_search.rst
index de66f8bc02906..cdade7dd2f311 100644
--- a/doc/modules/grid_search.rst
+++ b/doc/modules/grid_search.rst
@@ -101,7 +101,7 @@ For each parameter, either a distribution over possible values or a list of
 discrete choices (which will be sampled uniformly) can be specified::
 
   [{'C': scipy.stats.expon(scale=100), 'gamma': scipy.stats.expon(scale=.1),
-    'kernel': ['rbf'], 'class_weight':['auto', None]}]
+    'kernel': ['rbf'], 'class_weight':['balanced', None]}]
 
 This example uses the ``scipy.stats`` module, which contains many useful
 distributions for sampling parameters, such as ``expon``, ``gamma``,
diff --git a/sklearn/svm/classes.py b/sklearn/svm/classes.py
index 13f96b18041cb..cb3f542f6916b 100644
--- a/sklearn/svm/classes.py
+++ b/sklearn/svm/classes.py
@@ -588,12 +588,12 @@ class NuSVC(BaseSVC):
     cache_size : float, optional
         Specify the size of the kernel cache (in MB).
 
-    class_weight : {dict, 'auto'}, optional
+    class_weight : {dict, 'balanced'}, optional
         Set the parameter C of class i to class_weight[i]*C for
         SVC. If not given, all classes are supposed to have
-        weight one. The 'auto' mode uses the values of y to
-        automatically adjust weights inversely proportional to
-        class frequencies.
+        weight one. The "balanced" mode uses the values of y to automatically adjust
+        weights inversely proportional to class frequencies as
+        ``n_samples / (n_classes * np.bincount(y))``
 
     verbose : bool, default: False
         Enable verbose output. Note that this setting takes advantage of a