Pooling layers are essential components of Convolutional Neural Networks (CNNs) that help reduce the spatial dimensions of feature maps generated by convolutional layers. By downsampling the feature maps, pooling layers introduce translational invariance, allowing the network to recognize features regardless of their position in the input image. This process minimizes the number of parameters and computations, which can help prevent overfitting and improve model generalization. Common pooling techniques include max pooling, which retains the maximum value from a region, and average pooling, which computes the average value, both contributing to a more efficient and robust CNN architecture.