Seq2Seq provides modular, reusable layers and built-in models for seq2seq learning with Keras. It implements encoder-decoder architectures where the encoder converts an input sequence to a context vector and the decoder generates the output sequence. The library supports stacked LSTMs on both encoder and decoder sides, customizable depth, and can be integrated as layers into larger models for tasks like machine translation, chatbot development, and parsing.