Why is one model usually not sufficient in the modeling phase?

One model is usually not sufficient in the modeling phase because real-world systems are often complex and multifaceted, requiring different perspectives and approaches to capture their intricacies. Multiple models can accommodate various assumptions, scenariOS, and variables, providing a more comprehensive understanding of the problem. Additionally, different models can help validate results against each other, leading to more robust conclusions and better decision-making. Using a diverse set of models also allows for flexibility in addressing changing conditions and new information.