diff --git a/h2o-algos/src/main/java/hex/knn/KNN.java b/h2o-algos/src/main/java/hex/knn/KNN.java
index dcfb2cf46dc7..ae2ede5def64 100644
--- a/h2o-algos/src/main/java/hex/knn/KNN.java
+++ b/h2o-algos/src/main/java/hex/knn/KNN.java
@@ -2,6 +2,7 @@
 
 import hex.*;
 import water.DKV;
+import water.Key;
 import water.Scope;
 import water.fvec.Chunk;
 import water.fvec.Frame;
@@ -47,7 +48,7 @@ class KNNDriver extends Driver {
         @Override
         public void computeImpl() {
             KNNModel model = null;
-            Frame result = null;
+            Frame result = new Frame(Key.make("KNN_distances"));
             Frame tmpResult = null;
             try {
                 init(true);   // Initialize parameters
@@ -64,6 +65,7 @@ public void computeImpl() {
                 int responseColumnIndex = train.find(responseColumn);
                 int nChunks = train.anyVec().nChunks();
                 int nCols = train.numCols();
+                // split data into chunks to calculate distances in parallel task
                 for (int i = 0; i < nChunks; i++) {
                     Chunk[] query = new Chunk[nCols];
                     for (int j = 0; j < nCols; j++) {
@@ -71,13 +73,9 @@ public void computeImpl() {
                     }
                     KNNDistanceTask task = new KNNDistanceTask(_parms._k, query, _parms._distance, idColumnIndex, idColumn, idType, responseColumnIndex, responseColumn);
                     tmpResult = task.doAll(train).outputFrame();
-                    if (result == null) {
-                        result = tmpResult;
-                    } else {
-                        result = result.add(tmpResult);
-                    }
+                    // merge result from a chunk
+                    result = result.add(tmpResult);
                 }
-                result = result.deepCopy("KNN_distances");
                 DKV.put(result._key, result);
                 model._output.setDistancesKey(result._key);
                 Scope.untrack(result);
diff --git a/h2o-algos/src/main/java/hex/knn/KNNDistanceTask.java b/h2o-algos/src/main/java/hex/knn/KNNDistanceTask.java
index 13bc78ee215f..31bca772ede3 100644
--- a/h2o-algos/src/main/java/hex/knn/KNNDistanceTask.java
+++ b/h2o-algos/src/main/java/hex/knn/KNNDistanceTask.java
@@ -21,7 +21,7 @@ public class KNNDistanceTask extends MRTask<KNNDistanceTask>  {
     public byte _idColumnType;
 
     /**
-     *
+     * Calculate distances dor a particular chunk
      */
     public KNNDistanceTask(int k, Chunk[] query, KNNDistance distance, int idIndex, String idColumn, byte idType, int responseIndex, String responseColumn){
         this._k = k;
@@ -70,9 +70,13 @@ public void reduce(KNNDistanceTask mrt) {
         this._topNNeighboursMaps.reduce(inputMap);
     }
 
+    /**
+     * Get data from maps to Frame
+     * @param vecs
+     * @return filled array of vecs with calculated data 
+     */
     public Vec[] fillVecs(Vec[] vecs){
         for (int i = 0; i < vecs[0].length(); i++) {
-            // id is on 0 index in vecs
             String id = _idColumnType == Vec.T_STR ? vecs[0].stringAt(i) : String.valueOf(vecs[0].at8(i));
             TopNTreeMap<KNNKey, Object> topNMap = _topNNeighboursMaps.get(id);
             Iterator<KNNKey> distances = topNMap.keySet().stream().iterator();
@@ -92,6 +96,10 @@ public Vec[] fillVecs(Vec[] vecs){
         return vecs;
     }
 
+    /**
+     * Generate output frame with calculated distances.
+     * @return
+     */
     public Frame outputFrame() {
         int newVecsSize = _k*3+1;
         Vec[] vecs = new Vec[newVecsSize];
@@ -108,9 +116,10 @@ public Frame outputFrame() {
         vecs[0] = id;
         names[0] = _idColumn;
         for (int i = 1; i < _k+1; i++) {
-            names[i] = "dist_"+i;
-            names[_k+i] = _idColumn+"_"+i;
-            names[2*_k+i] = _responseColumn+"_"+i;
+            // names of columns
+            names[i] = "dist_"+i; // this could be customized
+            names[_k+i] = _idColumn+"_"+i; // this could be customized
+            names[2*_k+i] = _responseColumn+"_"+i; // this could be customized
             vecs[i] = id.makeZero();
             vecs[i] = vecs[i].toNumericVec();
             vecs[_k+i] = id.makeZero();
diff --git a/h2o-algos/src/main/java/hex/knn/KNNScoringTask.java b/h2o-algos/src/main/java/hex/knn/KNNScoringTask.java
index 7758dd6c2234..8838e95c288c 100644
--- a/h2o-algos/src/main/java/hex/knn/KNNScoringTask.java
+++ b/h2o-algos/src/main/java/hex/knn/KNNScoringTask.java
@@ -18,7 +18,7 @@ public class KNNScoringTask extends MRTask<KNNScoringTask> {
     public int _domainSize;
 
     /**
-     *
+     * Go through the whole input frame to find the k near distances and score based on them.
      */
     public KNNScoringTask(double[] query, int k, int domainSize, KNNDistance distance, int idIndex, byte idType, int responseIndex){
         this._k = k;
@@ -62,11 +62,13 @@ public double[] score(){
         for (int value: _distancesMap.values()){
             scores[value+1]++;
         }
+        // normalize the result score by _k
         for (int i = 1; i < _domainSize+1; i++) {
             if(scores[i] != 0) {
                 scores[i] = scores[i]/_k;
             }
         }
+        // decide the class by the max score
         scores[0] = ArrayUtils.maxIndex(scores)-1;
         return scores;
     }