Which Amazon SageMaker algorithm is appropriate when a model must both identify an object in an image and return its location within the image?

Object detection is the correct choice because it is specifically designed for tasks that involve both identifying objects within an image and pinpointing their exact locations using bounding boxes. This aligns perfectly with the requirement to "identify an object in an image and return its location." Image classification, while identifying objects, only assigns a label to the entire image and does not provide location information. XGBoost is a supervised learning algorithm primarily used for tabular data and regression/classification tasks, not image analysis. K-nearest neighbors (k-NN) is a non-parametric, lazy learning algorithm used for classification and regression, also not suited for identifying and localizing objects in images.