Sequence-to-sequence learning is a neural network framework designed to transform a given sequence into another sequence, which is particularly useful in tasks like machine translation, text summarization, and speech recognition. It typically employs encoder-decoder architectures, often enhanced with attention mechanisms, to handle variable-length input and output sequences effectively.