diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md
index dfdf6216b270c..eedc23424ad54 100644
--- a/docs/mllib-collaborative-filtering.md
+++ b/docs/mllib-collaborative-filtering.md
@@ -77,7 +77,7 @@ val ratings = data.map(_.split(',') match { case Array(user, item, rate) =>
 
 // Build the recommendation model using ALS
 val rank = 10
-val numIterations = 20
+val numIterations = 10
 val model = ALS.train(ratings, rank, numIterations, 0.01)
 
 // Evaluate the model on rating data
@@ -149,7 +149,7 @@ public class CollaborativeFiltering {
 
     // Build the recommendation model using ALS
     int rank = 10;
-    int numIterations = 20;
+    int numIterations = 10;
     MatrixFactorizationModel model = ALS.train(JavaRDD.toRDD(ratings), rank, numIterations, 0.01); 
 
     // Evaluate the model on rating data
@@ -210,7 +210,7 @@ ratings = data.map(lambda l: l.split(',')).map(lambda l: Rating(int(l[0]), int(l
 
 # Build the recommendation model using Alternating Least Squares
 rank = 10
-numIterations = 20
+numIterations = 10
 model = ALS.train(ratings, rank, numIterations)
 
 # Evaluate the model on training data
diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md
index 2a2a7c13186d8..3927d65fbf8fb 100644
--- a/docs/mllib-linear-methods.md
+++ b/docs/mllib-linear-methods.md
@@ -499,7 +499,7 @@ Note that the Python API does not yet support multiclass classification and mode
 will in the future.
 
 {% highlight python %}
-from pyspark.mllib.classification import LogisticRegressionWithLBFGS
+from pyspark.mllib.classification import LogisticRegressionWithLBFGS, LogisticRegressionModel
 from pyspark.mllib.regression import LabeledPoint
 from numpy import array
 
@@ -518,6 +518,10 @@ model = LogisticRegressionWithLBFGS.train(parsedData)
 labelsAndPreds = parsedData.map(lambda p: (p.label, model.predict(p.features)))
 trainErr = labelsAndPreds.filter(lambda (v, p): v != p).count() / float(parsedData.count())
 print("Training Error = " + str(trainErr))
+
+# Save and load model
+model.save(sc, "myModelPath")
+sameModel = LogisticRegressionModel.load(sc, "myModelPath")
 {% endhighlight %}
 </div>
 </div>
@@ -668,7 +672,7 @@ values. We compute the mean squared error at the end to evaluate
 Note that the Python API does not yet support model save/load but will in the future.
 
 {% highlight python %}
-from pyspark.mllib.regression import LabeledPoint, LinearRegressionWithSGD
+from pyspark.mllib.regression import LabeledPoint, LinearRegressionWithSGD, LinearRegressionModel
 from numpy import array
 
 # Load and parse the data
@@ -686,6 +690,10 @@ model = LinearRegressionWithSGD.train(parsedData)
 valuesAndPreds = parsedData.map(lambda p: (p.label, model.predict(p.features)))
 MSE = valuesAndPreds.map(lambda (v, p): (v - p)**2).reduce(lambda x, y: x + y) / valuesAndPreds.count()
 print("Mean Squared Error = " + str(MSE))
+
+# Save and load model
+model.save(sc, "myModelPath")
+sameModel = LinearRegressionModel.load(sc, "myModelPath")
 {% endhighlight %}
 </div>
 </div>
diff --git a/docs/mllib-naive-bayes.md b/docs/mllib-naive-bayes.md
index bf6d124fd5d8d..e73bd30f3a90a 100644
--- a/docs/mllib-naive-bayes.md
+++ b/docs/mllib-naive-bayes.md
@@ -119,7 +119,7 @@ used for evaluation and prediction.
 Note that the Python API does not yet support model save/load but will in the future.
 
 {% highlight python %}
-from pyspark.mllib.classification import NaiveBayes
+from pyspark.mllib.classification import NaiveBayes, NaiveBayesModel
 from pyspark.mllib.linalg import Vectors
 from pyspark.mllib.regression import LabeledPoint
 
@@ -140,6 +140,10 @@ model = NaiveBayes.train(training, 1.0)
 # Make prediction and test accuracy.
 predictionAndLabel = test.map(lambda p : (model.predict(p.features), p.label))
 accuracy = 1.0 * predictionAndLabel.filter(lambda (x, v): x == v).count() / test.count()
+
+# Save and load model
+model.save(sc, "myModelPath")
+sameModel = NaiveBayesModel.load(sc, "myModelPath")
 {% endhighlight %}
 
 </div>