Non-maximum Suppression (NMS) is a technique used in computer vision to eliminate redundant or overlapping bounding boxes in object detection tasks, ensuring that only the most relevant detections are retained. By selecting the bounding box with the highest confidence score and suppressing others that have a high overlap with it, NMS enhances the precision of object localization in images.