Outlier detection in Elastic machine learning is designed to identify abnormal data patterns across various applications, such as security analytics and fraud detection, without the necessity for a time-based analysis. Elastic's tool utilizes an ensemble of learners, combining nearest neighbor and density-based methods, to effectively detect outliers in multi-dimensional datasets. The system automatically calibrates hyperparameters and leverages ensemble learning to enhance predictive performance and scalability, making it user-friendly by not requiring manual parameter settings. Elastic's approach to outlier detection is benchmarked against publicly available datasets, showing competitive performance with state-of-the-art algorithms, despite certain limitations like downsampling that might lead to false positives in densely clustered data. The tool's performance is particularly notable in conditions with varying concentrations of outliers, though it can be outperformed by algorithms like KNN and LOF when specific parameters are manually adjusted. Elastic's outlier detection offers a streamlined solution for extracting insights from non-time-indexed data, enhancing the ability to discover actionable insights from diverse datasets.