Locality-Sensitive Hashing (LSH) is a technique used to approximate nearest neighbor searches in high-dimensional spaces by hashing input items so that similar items map to the same 'buckets' with high probability. This method significantly reduces the dimensionality of data, enabling efficient similarity searches in large datasets while sacrificing some precision for speed.