The Scale-Invariant Feature Transform (SIFT) is a computer vision algorithm used to detect and describe local features in images. It identifies key points in an image that remain consistent across different scales and orientations, making it robust to changes in viewpoint and lighting. SIFT is particularly useful for tasks like object recognition and image stitching. SIFT works by first detecting key points using a difference-of-Gaussians method, then assigning a descriptor to each key point based on the local image gradients. This allows for effective matching of features between different images, even if they are taken under varying conditions.