diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
index a741584982725..38ca2af80fa4a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
@@ -77,6 +77,34 @@ class KMeansModel @Since("1.1.0") (@Since("1.0.0") val clusterCenters: Array[Vec
   def predict(points: JavaRDD[Vector]): JavaRDD[java.lang.Integer] =
     predict(points.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Integer]]
 
+  /**
+   * Returns the distances to all clusters for a given point.
+   */
+  @Since("1.5.0")
+  def distanceToCenters(point: Vector): Iterable[(Int, Double)] = {
+    val pointWithNorm = new VectorWithNorm(point)
+    clusterCentersWithNorm.zipWithIndex.map {
+      case (c, i) =>
+        (i, KMeans.fastSquaredDistance(c, pointWithNorm))
+    }.toList
+  }
+
+  /**
+   * Maps given points to their distances to all clusters.
+   */
+  @Since("1.5.0")
+  def distanceToCenters(points: RDD[Vector]): RDD[(Vector, Iterable[(Int, Double)])] = {
+    val centersWithNorm = clusterCentersWithNorm
+    val bcCentersWithNorm = points.context.broadcast(centersWithNorm)
+    points.map(p => {
+      val pointWithNorm = new VectorWithNorm(p)
+      (p, bcCentersWithNorm.value.zipWithIndex.map {
+        case (c, i) =>
+          (i, KMeans.fastSquaredDistance(c, pointWithNorm))
+      }.toList)
+    })
+  }
+
   /**
    * Return the K-means cost (sum of squared distances of points to their nearest center) for this
    * model on the given data.
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala
index 3003c62d9876c..9b45e88e61b35 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala
@@ -250,7 +250,8 @@ class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext {
 
     for (initMode <- Seq(RANDOM, K_MEANS_PARALLEL)) {
       // Two iterations are sufficient no matter where the initial centers are.
-      val model = KMeans.train(rdd, k = 2, maxIterations = 2, runs = 1, initMode)
+      val k = 2
+      val model = KMeans.train(rdd, k = k, maxIterations = 2, runs = 1, initMode)
 
       val predicts = model.predict(rdd).collect()
 
@@ -259,6 +260,8 @@ class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext {
       assert(predicts(3) === predicts(4))
       assert(predicts(3) === predicts(5))
       assert(predicts(0) != predicts(3))
+
+      assert(model.distanceToCenters(rdd).flatMap(_._2).count === points.size * k)
     }
   }
 
@@ -341,6 +344,7 @@ class KMeansClusterSuite extends SparkFunSuite with LocalClusterSparkContext {
       val model = KMeans.train(points, 2, 2, 1, initMode)
       val predictions = model.predict(points).collect()
       val cost = model.computeCost(points)
+      val dToCenters = model.distanceToCenters(points.first())
     }
   }
 }