Encoder-Decoder Networks are a class of neural architectures designed to handle sequence-to-sequence tasks by first encoding an input sequence into a fixed-length context vector, and then decoding that vector into an output sequence. They are widely used in applications like machine translation, where the model learns to map sequences from one domain to another while maintaining contextual integrity.