Variable selection is a statistical technique used in the field of data analysis and machine learning to identify a subset of relevant features or predictors from a larger set of variables. The goal of variable selection is to improve the predictive accuracy and interpretability of a model by reducing overfitting and identifying the most important variables that have the most impact on the outcome. There are various methods for variable selection, including univariate feature selection, recursive feature elimination, and regularization techniques such as Lasso and Ridge regression. These methods help researchers and data scientists to identify the most important variables and build more efficient and accurate predictive models. Variable selection is commonly used in fields such as biology, finance, and social sciences to identify key factors influencing a particular outcome or phenomenon.