Major health problem affecting millions of women worldwide is breast cancer. Enhancing patient outcomes and lowering death rates are directly related to early identification and precise prognosis of breast cancer. So as to improve breast cancer prediction, this research compares machine learning algorithms as well as cross-validation methods. The study makes use of an extensive dataset that includes the clinical characteristics of patients with breast cancer. To create prediction models, a assortment of machine learning methods are used, like decision trees, KNN, random forests, logistic regression, and support vector machines. In addition, an assortment of cross-validation procedures are worn to evaluate model presentation, including leave-p-out cross-validating, stratified k-fold cross-validation, and k-fold cross-validation. The goal of the comparison analysis is to calculate about a mixture of cross-validation methods and machine learning algorithms predict breast cancer. Performance indicators are used to consider every model's predictive power, including recall, accuracy, precision, and f1 score.