The LASSO method is a powerful tool for selecting important variables, handling complex data, and solving multicollinearity issues in regression analysis. By adding a penalty term to the model estimation, LASSO can effectively remove unnecessary variables from the model, improving model interpretation and prediction accuracy. Comparisons with other methods like optimal subsets and ridge regression show that LASSO and adaptive LASSO perform well in addressing multicollinearity problems and enhancing model performance.