The goal of model validation is to evaluate whether the numerical results quantifying the hypothesized estimations/predictions of the trained model are acceptable descriptions of an independent dataset. The main reason is that any measure on the training set would be biased and optimistic since the model has already seen those observations. If we don't have a different dataset for validation, we can hold one fold of the data out from training and use it as benchmark. Another common technique is the cross-fold validation, and its stratified version, where the whole historical dataset is split into multiple folds. For simplicity, we will discuss the hold-one-out method; the same criteria apply also to the cross-fold validation.
The splitting into training and validation set cannot be purely random. The validation set should represent the future hypothetical scenario in which we will use the model for scoring. It is important not to contaminate the validation set with information...