Co-Scheduling jobs in High Performance Computing (HPC) systems offers significant potential to improve system throughput and energy efficiency. However, resource contention in shared node resources can introduce performance degradation, leading to job slowdowns and counteracting these benefits. To address this challenge, sophisticated co-scheduling algorithms must be developed, requiring a good understanding of the submitted applications to make informed scheduling decisions. In this work, we classify and present a number of performance models that can be leveraged to support advanced co-scheduling strategies. The methods focus on either assigning specific tags to applications or predicting their potential speedup or slowdown when co-executed with other workloads. To achieve this, we explore both empirical approaches and Machine Learning-based techniques, assessing their respective benefits and limitations. Furthermore, we discuss key trade-offs that arise when selecting and building the most suitable model for beneficial co-location prediction in HPC environments. Finally, we provide preliminary results demonstrating the effectiveness of each model through representative examples across multiple model categories.