Detection of phishing websites is one of the very important duties which relates to concerns in computer security since individuals with malicious intent have gone on the internet to trick users to disclose their private data. Over the years, data mining, and especially machine learning, has proven effective in tackling this problem through URL, SSL, and traffic feature analysis. The paper studies and compares performance of three machine learning algorithms randomly selecting among RandomForest, ExtraTrees, and hybrid Voting Classifier incorporating RandomForest and ExtraTrees. The results obtained were based on the analysis of models with the Phishing Websites Dataset that contained 11,055 samples and 32 features. For Random Forest, the accuracy of the model was 96.74 percentage while Extra Trees scored 96.63 percentage and the Voting Classifier with Random Forest, Extra Trees, Decision Tree, and multi-layer perceptron integrated obtained 97.15 percentage accuracy. The comparisons revealed that the Voting Classifier had lower false negative and false positive rate compared to the rest of the models. Feature importance analysis identified three features that are essential for phishing detection: SSLfinal-State, URL-of-Anchor, and web-traffic. This paper also discusses the interplay between computational complexity, significance of different features, and the error patterns. Based on our careful analysis, we can say that use of ensemble methods, especially hybrid ones, is probably the most effective way of providing accurate results in many evaluation tasks, including phishing detection. In addition, the research highlights the importance of model interpretability by way of feature importance and offers insights on deep learning and hybrid designs to enhance phishing detection.