In the context of financial security, detection of fraud in credit card is an essential problem to identify in the present scenario. With machine learning algorithms the fraudulent problem can be reduced. Machine learning algorithms focus on bolstering the accuracy of fraud detection models through hyperparameter optimization and cross-validation techniques. Utilizing methods like randomized search for hyperparameter tuning alongside cross-validation, the study systematically evaluates classification algorithms such as the Decision Tree classifier and XGBoost algorithm. Fraud detection effectiveness is measured using performance indicators such as accuracy, recall, f1-score, and precision. The approach aims to enhance model generalization and adaptability to diverse fraud patterns with the least amount of false negatives and false positives. Remarkably, the algorithms demonstrate superior performance when employing hyperparameters compared to without. Moreover, it explores the impact of feature engineering on model performance and investigates ensemble learning techniques to create more resilient fraud detection systems. This comprehensive analysis strives to deepen our knowledge of how to make credit card fraud detection models more effective, which helps to develop strong financial security systems.