Dropout is a regularization technique used in training neural networks to prevent overfitting. During training, dropout randomly "drops" (sets to zero) a proportion of neurons in the network, forcing the model to learn more robust features and reducing reliance on specific neurons. It is particularly effective in deep learning models and helps generalize the network better to unseen data.