K-nearest neighbors is a straightforward algorithm used for classification and regression tasks. It works by finding the 'k' closest data points (neighbors) to a given point and assigning its class based on the majority class among those neighbors. This method is intuitive and easy to understand but can become computationally expensive with large datasets. It's often compared to other algorithms like decision trees or support vector machines for its simplicity.