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.