What is overfitting, and how do you prevent it?

Overfitting is when a model learns the training data too closely — including its noise — so it performs well on training data but poorly on new data. Signs: high training accuracy, low validation accuracy. Prevent it with more/represented data, simpler models, regularisation (L1/L2, dropout), early stopping, cross-validation, and holding out a genuine test set. The core idea is to reward generalisation, not memorisation.