A convolution operation is a mathematical operation performed in various fields, including signal processing and image analysis. It involves combining two functions to produce a third function that represents the overlap between them. In the context of Convolutional Neural Networks (CNNs), a convolution operation is used to extract features from input data, such as images. It involves sliding a small filter or kernel over the input data and computing the dot product between the filter and the corresponding input values at each location. This process helps capture local patterns and spatial relationships within the data. By applying multiple convolutions with different filters, CNNs can detect various features and learn hierarchical representations of the input data.