LASSO, which stands for Least Absolute Shrinkage and Selection Operator, is a regression analysis method that performs both variable selection and regularization to enhance the prediction accuracy and interpretability of statistical models. It achieves this by imposing a constraint on the sum of the absolute values of the model coefficients, effectively shrinking some coefficients to zero and thereby selecting a simpler model that avoids overfitting.