Final answer:
Elastic Net combines Lasso and Ridge regularization methods in linear regression, aiming to utilize both L1 and L2 penalties. The downsides include increased computational complexity and the need for tuning an additional hyperparameter, as well as a potential risk of overfitting when there are more features than observations.
Step-by-step explanation:
The Elastic Net is a regularization technique used in linear regression that combines both Lasso (Least Absolute Shrinkage and Selection Operator) and Ridge (also known as Tikhonov regularization) methods. By incorporating both L1 and L2 regularization terms, Elastic Net aims to benefit from the strengths of both methods. Lasso is good for generating sparse solutions, which simplifies models by reducing the number of variables, whereas Ridge is great for dealing with multicollinearity between features.
One downside of Elastic Net is the added computational complexity. The inclusion of two regularization terms means there's an extra hyperparameter to tune, and finding the optimal balance between the L1 and L2 penalties can be more challenging and time-consuming than using Lasso or Ridge regularization independently. Additionally, Elastic Net may still experience issues in certain scenarios, such as when there are more features than observations, potentially leading to overfitting.