The increasing use of AI-generated code underscores the need for effective detection systems. However, their performance often deteriorates when faced with distribution shifts. This paper presents our system for SemEval-2026 Task 13: A, which focuses on binary classification of human-written versus machine-generated code across various programming languages and domains. We systematically compare traditional classifiers, such as Random Forest and XGBoost, which utilize statistical and TF-IDF features, against pipelines that leverage frozen embeddings from advanced code transformers like UniXcoder and GraphCodeBERT. Our results reveal a notable trade-off, i.e., while transformer-based pipelines excel in in-distribution validation (reaching up to 0.89 Macro F1), they experience severe performance drops up to 77%; when applied to out-of-distribution languages and domains. In contrast, models employing TF-IDF with tree-based classifiers demonstrate significantly greater stability. We identify this fragility as a result of a bias toward superficial formatting, particularly whitespace, which is accentuated by transformers. By implementing simple space normalization, we enhance the out-of-distribution robustness of traditional models; however, this also highlights the ongoing dependence of embeddings on these non-semantic features. Our findings suggest that for creating generalizable code detection systems, straightforward, well-normalized lexical features may be more reliable than complex, unrefined embeddings.