Using both L1 and L2 regularization together can really boost machine learning models. Each method has its own strengths, and when they work together, they can make a big difference.

1. Choosing Important Features:

   • L1 regularization, also known as Lasso, helps pick out the most important features. It can make some numbers in the model exactly zero, meaning those features won't be used at all. This is helpful when you have a lot of extra features that don't really matter. L1 helps keep only the ones that really count.

2. Making Models Smoother and More Stable:

   • L2 regularization, called Ridge, works a bit differently. It tries to reduce the size of the large numbers in the model. This makes the model smoother and helps it stay steady instead of getting too complex. It's useful when all features are important, but some matter more than others.

3. Getting the Best of Both Worlds:

   • When you use both methods together, it's known as Elastic Net. This combination can create stronger models. It helps choose the best features while also keeping the numbers in check. As a result, the model can work better with new data it hasn't seen before.

By using this combination, complicated algorithms like linear regression or logistic regression become easier to understand and more effective.