A Convolutional Layer is a fundamental building block of Convolutional Neural Networks (CNNs) that applies convolution operations to input data, allowing the network to automatically and adaptively learn spatial hierarchies of features through backpropagation. It is particularly effective for processing data with grid-like topology, such as images, by preserving spatial relationships between pixels.