Answer - Cross-validation in Deep Learning (DL) can be tricky because most of the CV techniques need training the model at least a couple of times. However, in deep learning, you would normally tempt to avoid CV because of the cost associated with training k different models. Rather than doing k-Fold or other CV techniques, you might use a random subset of your training data as a hold-out for validation purposes. For example, Keras’s deep learning library enables you to pass one of two parameters for the fit function that performs training. This covers: Firstly, validation_split: percentage of the data that should be held out for validation Secondly, validation_data: a tuple of (X, y) which should be used for validation. This parameter overrides the validation_split parameter which means you can use only one of these parameters at once. And, the same method is used in other DL frameworks such as PyTorch and MxNet. They also suggest giving the dataset into three parts: training, validation, and testing.