Elastic Net is a combination of Ridge and Lasso regression. It adds both L1 and L2 regularization terms to the cost function. This provides a balance between the two regularization methods.

👉 What it does: Combines L1 + L2 penalties → balance between shrinkage and feature selection

✅ When to use:

• You have:
  • Many features
  • Correlated features
• You want:
  • Some feature selection
  • But also stability like Ridge

🧠 Real-life examples:

• Predicting user churn with hundreds of behavioral metrics
• Credit scoring with many correlated financial variables
• Text analysis (NLP) with thousands of features (words)

💡 Key idea: 👉 Best of both worlds