Doc2Vec is a machine learning model that extends the idea of Word2Vec to represent entire documents as fixed-length vectors. It captures the semantic meaning of documents by considering the context of words within them, allowing for better understanding and comparison of text data. The model works by training on a large corpus of text, learning to associate each document with a unique vector while also representing the words within it. This enables tasks like document classification, clustering, and information retrieval, making Doc2Vec a valuable tool in natural language processing.