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The talk will discuss modeling requirements in modern power systems, especially in light of growing renewable generation, reduced system inertia, and increasing operational uncertainty. A main focus will be on using system-identification and prediction-error methods to monitor electromechanical oscillations.The special focus will be on assessing uncertainty of the obtained mathematical models and factors that influence this uncertainty. Furthermore, the talk will address strategies for experiment design in system identification using prediction-error methods, with an emphasis on obtaining models with minimal uncertainty while respecting the constraints present in real power grids.Finally, the role of machine-learning methods in data- driven modeling will be examined, including where they are effective, where they have limitations, and how they can complement classical identification techniques in power-system applications.