Word2vec is a technique in natural language processing for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words, following the principles of distributional semantics. Once trained, the model can be used to find words with similar meanings or usage, while its embeddings can serve as inputs to systems for search and classification.