SAS Selection is a method used in statistical analysis to choose a subset of variables or features from a larger set. This process helps in simplifying models, improving performance, and enhancing interpretability by focusing on the most relevant data. It is commonly applied in fields like machine learning and data mining. The selection process can involve various techniques, such as forward selection, backward elimination, and stepwise selection. Each technique has its own approach to adding or removing variables based on specific criteria, such as statistical significance or predictive power, ultimately leading to a more efficient model.