Siamese Networks are a type of neural network architecture that use two or more identical subnetworks to process input data in parallel, primarily designed for tasks like similarity learning and verification. They are effective in scenarios where comparing two inputs is necessary, such as facial recognition or signature verification, by learning a meaningful distance metric between the inputs.