Elastic Net Regression is a regularization technique that combines the approaches of Lasso and Ridge regression. Here are some key points about Elastic Net:

• Like Lasso, it performs automatic variable selection by driving some coefficients to zero.

• But similar to Ridge, it also handles groups of correlated predictors by combining both L1 and L2 penalties.

• The regularization term is a linear combination of L1 and L2 norms: (1-α)L2 + αL1

• α tunes the relative contribution of L1 vs L2 penalty between 0-1.

• α=1 recovers Lasso, α=0 recovers Ridge regression.

• It overcomes limitations of Lasso by allowing groups of correlated features to be selected together.

• Performs better than Lasso in situations with highly correlated features.

• The grouping effect makes coefficients more stable and parameter estimation consistent even with numerous predictors.

• Useful as a compromise between sparsity of Lasso and grouping effect of Ridge regularization.

• Hyperparameters like α and lambda need to be tuned for best performance.

So in summary, Elastic Net achieves sparsity and grouping effect simultaneously, making it a flexible regression model for high-dimensional variable selection.