I am currently studying about the methods by which I can estimate the test error while building the model on training data.While studying I came across two methods first is **validation approach** and other is **k-fold cross validation** approach.

**Validation approach**- In this we randomly divide the given data set of samples into two parts

- training set
- validation set

**k-fold cross validation**- In this we randomly divide the data into K equal-sized parts. We leave out part k, fit the model to the other K - 1 parts (combined), and then obtain predictions for the left-out kth part.

I want to know the limitations of validation approach over k-fold cross validation approach.