Elastic Net Regression is a hybrid model that synergistically combines the strengths of Lasso and Ridge regression. It performs robust feature selection by shrinking irrelevant coefficients to zero, while also effectively handling multicollinearity by grouping correlated features. This makes it a superior and stable tool for building interpretable predictive models on complex, high-dimensional datasets commonly found in fields like genomics and finance.