With the proliferation of Internet of Things (IoT) devices, securing these interconnected systems against cyberattacks has become a critical challenge. Traditional security paradigms often fail to cope with the scale and diversity of IoT network traffic. This paper presents a comparative benchmark of classic machine learning (ML) and state-of-the-art deep learning (DL) algorithms for IoT intrusion detection. Our methodology employs a two-phased approach: a preliminary pilot study using a custom-generated dataset to establish baselines, followed by a comprehensive evaluation on the large-scale CICIoTDataset2023. We benchmarked algorithms including Random Forest, XGBoost, CNN, and Stacked LSTM. The results indicate that while top-performing models from both categories achieve over 99% classification accuracy, this metric masks a crucial performance trade-off. We demonstrate that tree-based ML ensembles exhibit superior precision (91%) in identifying benign traffic, making them effective at reducing false positives. Conversely, DL models demonstrate superior recall (96%), making them better suited for minimizing the interruption of legitimate traffic. We conclude that the selection of an optimal model is not merely a matter of maximizing accuracy but is a strategic choice dependent on the specific security priority either minimizing false alarms or ensuring service availability. This work provides a practical framework for deploying context-aware security solutions in diverse IoT environments.