Designing sophisticated artificial intelligence systems depends critically on model selection, as it greatly affects the accuracy and generalizability of the ultimate product. Choosing the proper model that has a balance among complexity, interpretability, and computational efficiency is sometimes the make-or-break of a machine learning initiative. Choosing the proper model for a particular issue depends on several elements, including the data type, the field of the problem, the availability of computational resources, as well as the desired performance measures. From easy linear regression exercises to difficult image recognition problems, each of which might call for a different approach to machine learning. Model selection also involves evaluating several trade-offs between overfitting and underfitting. In Chapter 1 , we had a brief discussion about various machine learning models. Here, we will discuss several model selection methods and the advantages and disadvantages of different model types, and we will offer suggestions on selecting the most appropriate model for various machine learning applications.