Choosing the right outlier or novelty detection method depends on your data and goals:

• Dimensionality: For low-dimensional, well-structured data, use classical statistical methods like robust covariance or Mahalanobis distance;
• High-dimensional or nonlinear data: Prefer tree-based methods such as Isolation Forest or boundary-based methods like One-Class SVM;
• Clusters or varying densities: Density-based methods such as Local Outlier Factor (LOF) are often more effective.

Also consider your objectives:

• If you need clear explanations for flagged cases, choose simpler models or those with interpretable decision boundaries;
• For high contamination rates, select robust methods that do not assume most data is normal;
• Decide whether you need novelty detection (finding new patterns) or outlier detection (flagging rare cases), as some algorithms are better suited for one or the other.