diff --git a/R-package/demo/README.md b/R-package/demo/README.md
index be08ee54f..5ee1aa797 100644
--- a/R-package/demo/README.md
+++ b/R-package/demo/README.md
@@ -1,6 +1,7 @@
 XGBoost R Feature Walkthrough
 ====
-* [Basic walkthrough of wrappers](basic_walkthrough.R) 
+* [Basic walkthrough of wrappers](basic_walkthrough.R)
+* [Train a xgboost model from caret library](caret_wrapper.R)
 * [Cutomize loss function, and evaluation metric](custom_objective.R)
 * [Boosting from existing prediction](boost_from_prediction.R)
 * [Predicting using first n trees](predict_first_ntree.R)
diff --git a/R-package/demo/caret_wrapper.R b/R-package/demo/caret_wrapper.R
index 5c53c9915..13e05e562 100644
--- a/R-package/demo/caret_wrapper.R
+++ b/R-package/demo/caret_wrapper.R
@@ -22,11 +22,14 @@ df[,AgeCat:= as.factor(ifelse(Age > 30, "Old", "Young"))]
 df[,ID:=NULL]
 
 #-------------Basic Training using XGBoost in caret Library-----------------
-# set up control parameters for caret::train
-# here we use 10-fold cross-validation, repeating twice
-fitControl <- trainControl(method = "cv", number = 10, repeats = 2)
+# Set up control parameters for caret::train
+# Here we use 10-fold cross-validation, repeating twice, and using random search for tuning hyper-parameters.
+fitControl <- trainControl(method = "cv", number = 10, repeats = 2, search = "random")
 # train a xgbTree model using caret::train
 model <- train(factor(Improved)~., data = df, method = "xgbTree", trControl = fitControl)
 
+# Instead of tree for our boosters, you can also fit a linear regression or logistic regression model using xgbLinear
+# model <- train(factor(Improved)~., data = df, method = "xgbLinear", trControl = fitControl)
+
 # See model results
-print(model)
\ No newline at end of file
+print(model)