Dropout is a regularization technique used in neural networks to prevent overfitting during training. It works by randomly “dropping out” a subset of neurons in the network during each training iteration, effectively ignoring them. This encourages the network to learn more robust features by not relying too heavily on any single neuron. Originally popularized in deep learning, dropout has been shown to improve model performance by promoting a more generalized understanding of the data. Although newer techniques like batch normalization have emerged, dropout remains a simple and effective method for enhancing neural network training.