A Sequence-to-Sequence Model is a type of neural network architecture designed to transform a given sequence of elements, such as words or characters, into another sequence, often used in tasks like language translation, summarization, and question answering. It typically employs an encoder-decoder structure, where the encoder processes the input sequence and the decoder generates the output sequence, often enhanced by attention mechanisms to improve performance.