An encoder-decoder model is a type of machine learning model that consists of two main components: an encoder and a decoder. The encoder processes the input data and produces a compressed representation of it, while the decoder takes that compressed representation and generates an output based on it. This type of model is often used in tasks such as machine translation, image captioning, and speech recognition. The encoder-decoder architecture has become popular due to its ability to handle variable-length inputs and outputs.