The K-Nearest Neighbors (KNN) algorithm, a supervised machine learning technique, is predominantly utilized for classification. This algorithm operates by classifying a new data point according to the categories of its closest neighbors within the training dataset.

In the context of classification, the KNN classifier designates a class to a new data point by identifying the 'k' nearest neighbors in the training set, with 'k' being a user-defined parameter. The classification of the new data point is then determined by a majority vote among these 'k' nearest neighbors.

Despite its simplicity and adaptability, the KNN algorithm is computationally intensive for extensive datasets. It necessitates a meticulous selection of both the 'k' value and the distance metric. Nonetheless, KNN remains a widely employed and effective tool for classification tasks in the realm of machine learning.