K-Nearest Neighbors (KNN) is a simple, supervised machine learning algorithm that can be used for classification and regression tasks. The algorithm works by finding the K closest data points (neighbors) to a given query point and making predictions based on the majority class (for classification) or the average value (for regression) of these neighbors. KNN is a non-parametric method, meaning it makes no assumptions about the underlying data distribution. It is easy to implement and understand, making it popular for various applications such as pattern recognition, recommendation systems, and medical diagnosis. However, KNN can be computationally intensive, especially with large datasets, as it requires calculating the distance between the query point and all training points. Additionally, the performance of KNN can be affected by the choice of K and the distance metric used.