Vector Search involves finding similar vectors within a collection by comparing their distances in a high-dimensional space. This approach is crucial in tasks where you need to retrieve similar items, such as images, documents, or user preferences, by understanding their feature representations. It's often utilized with vector stores or databases and supports algorithms like k-nearest neighbor (KNN). It complements the workings of embeddings and helps in refining search results.