Breast Cancer is one of the major causes of mortality in women worldwide. Accurate and timely diagnosis of breast cancer is of paramount importance for effective treatment and patient outcomes. Automated tools based on ML models can prove to be very helpful for doctors and pathologists in early detection of breast cancer and subsequent decision-making for treatment course. In this study, we have used the Breast Cancer Wisconsin Diagnostic Dataset. We have applied four ML algorithms that are Support Vector Machine (SVM), Logistic Regression, Decision Tree, and K-Nearest Neighbors (KNN), and also applied hyperparameter tuning and cross-validation on the models. We evaluated and compared the performance of the models after applying the techniques with research papers which conducted research on breast cancer detection consisting of the same ML models on the same dataset. The evaluation of model performance is done by using different performance metrics to evaluate the performances of the models. It was observed that support vector machine had the highest accuracy of 0.98 among all the ML models that we applied on the dataset.