A pooling layer is a type of layer used in convolutional neural networks (CNNs) to reduce the spatial dimensions of the input data, which in turn helps to decrease the computational cost and mitigate overfitting. Pooling operates by sliding a filter across the input and summarizing the features, often using operations like max pooling (selecting the maximum value in a region) or average pooling. For example, a max pooling layer with a 2x2 filter would slide across its input data and take the maximum value from each 2x2 block, effectively condensing the information while retaining critical features. This process helps to create a more abstract representation of the data and improves the model's ability to generalize.