Multiple Linear Regression with k-fold Cross Validation
As Peter said, you need to split your dataset into two subsets: Training, and Test sets. Generally, 80% of data is allocated for Training set (20% for the Test set).
Thereafter, depending on the language/package you use (caret in your case), you use 5- or 10-fold cross-validation to train your model, and finally, you check the prediction ability of the model using the Test set.

I quickly checked the Caret Package website and ripped the required code for you.

1- For Training-Test split:

4.1 Simple Splitting Based on the Outcome

library(caret)
set.seed(3456)
trainIndex &lt;- createDataPartition(iris$Species, p = .8, 
                                  list = FALSE, 
                                  times = 1)


2- For training with 10-fold cross-validation:

5.3 Basic Parameter Tuning,
5.5.4 The trainControl Function

fitControl &lt;- trainControl(## 10-fold CV
                       method = "repeatedcv",
                       number = 10,
                       ## repeated the CV ten times
                       repeats = 10)


Usually, we do the cross-validation only once (repeats = 1); but to check the consitency of the results, you may need more repeats.