Max pooling is a down-sampling technique used in convolutional neural networks to reduce the spatial dimensions of feature maps, thereby decreasing the number of parameters and computation in the network. It works by sliding a window across the input feature map and selecting the maximum value within each window, preserving the most prominent features while discarding less significant ones.