A. Regularization in deep learning is a technique used to prevent overfitting and improve the generalization of neural networks. It involves adding a regularization term to the loss function, which penalizes large weights or complex model architectures. Regularization methods such as L1 and L2 regularization, dropout, and batch normalization help control model complexity and improve its ability to generalize to unseen data.