### Q1: What is overfitting in machine learning, and why is it detrimental to model performance?  
### Q2: How can regularization techniques help in preventing overfitting in machine learning models?  
### Q3: What are some of the challenges you might face when trying to balance overfitting and underfitting, and how can you address them?  
### Q4: In what scenarios would you consider using cross-validation to enhance the performance of your models?  
### Q5: How might feature selection play a role in addressing issues of overfitting in your machine learning projects?  
### Q6: Can you describe a situation in which understanding underfitting could lead to better model performance?  
### Q7: How can effective communication of model performance issues impact team collaboration in machine learning projects?  
### Q8: What strategies would you employ to explain the concepts of overfitting and underfitting to a non-technical stakeholder?  
### Q9: How important is it to share your experiences with model performance challenges to foster a learning culture within your team?