Automatic classification of Polycystic Ovary Syndrome (PCOS) in ultrasound imaging is a significant topic in medical Artificial Intelligence (AI) research. In this paper, we present the benchmarking and results of the Auto-PCOS Classification Challenge, held from December 26, 2023, to March 1, 2024. The challenge was virtually organized by the Medical Imaging and Signal Analysis Hub (MISAHUB) in collaboration with the Department of Electronics and Communication Engineering, Indira Gandhi Delhi Technical University for Women (IGDTUW), the IEEE Women in Engineering IGDTUW branch, the Electron Devices Society Delhi branch, and the IEEE Delhi Section. The training and testing datasets developed and released for the challenge were benchmarked using ten machine learning models, including Random Forest, Ridge, Bagging, Multi-Layer Perceptron (MLP), K-Nearest Neighbors (KNN), Decision Tree, AdaBoost, Gaussian Naïve Bayes, XGBoost, and LightGBM, as well as ten transfer learning models: VGG19, Xception, ResNet50V2, InceptionV3, InceptionResNetV2, MobileNetV2, DenseNet169, NASNetMobile, and ConvNeXtBase. Among these models, XGBoost, LightGBM, and K-Nearest Neighbors achieved the highest performance among the machine learning models, while NASNetMobile outperformed other transfer learning models. Team AI Avengers emerged as the winner of the challenge.