Cross Validation for Beginners

While attempting to solve a ML problem, we do a train_test split. If this split is done randomly than it might be possible that some dataset might be completely present in test set and absent from training set or vice versa. This reduces the accuracy of model. So Cross Validation comes into picture. Cross-validation is a step in the process of building a machine learning model which helps us ensure that our models fit the data accurately and also ensures that we do not overfit.Cross-validation is dividing training data into a few parts.