Model selection is a process in statistics and machine learning that involves choosing the best model from a set of candidate models for a given dataset. This is typically done by evaluating the performance of each model based on certain criteria, such as accuracy, complexity, and generalization ability. Model selection aims to find the model that strikes the best balance between bias and variance, ultimately leading to better predictive performance on new, unseen data. It is an important step in the machine learning pipeline and can greatly impact the success of a predictive model.