What is overfitting and how can you avoid it in models?

Overfitting occurs when a model learns the training data too well, including its noise and random fluctuations, leading to excellent training accuracy but poor performance on unseen test data. To avoid this, I use several strategies. First, I apply regularization techniques like Lasso or Ridge to penalize large weights. Second, I utilize k-fold cross-validation to ensure the model generalizes well across different data splits. Third, for neural networks, I employ dropout to prevent reliance on specific neurons. Finally, I monitor validation loss and stop training early if performance plateaus.