Selective Search is an image segmentation technique used in computer vision to identify and locate objects within an image. It works by generating a set of potential bounding boxes around regions of interest, allowing for a more efficient analysis of the image. This method combines both color, texture, and size information to create a hierarchy of regions, which helps in distinguishing different objects. The process begins with over-segmenting the image into smaller regions using algorithms like Felzenszwalb's segmentation. These regions are then merged based on similarity criteria, resulting in a manageable number of candidate object proposals. Selective Search is widely used in object detection tasks, including those in deep learning frameworks.