5 Reasons why you should use Cross-Validation in your Data Science Projects

Cross-Validation is an essential tool in the Data Scientist toolbox. It allows us to utilize our data better. Before I present you my five reasons to use cross-validation, I want to briefly go over what cross-validation is and show some common strategies. The training set is used to train the model, and the validation/test set is used to validate it on data it has never seen before. The classic approach is to do a simple 80%-20% split, sometimes with different values like 70%-30% or 90%-10%.