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There are several possible reasons for a low AUC and ROC score for a significant model, including:

1. Insufficient sample size: If the sample size is small, the model may not have enough data to accurately predict the outcome.

2. Unbalanced data: If the dataset is imbalanced, meaning there are more instances of one class than the other, this can cause the model to perform poorly.

3. Incorrect model selection: Choosing the wrong type of model or using inappropriate hyperparameters may lead to a low AUC and ROC score.

4. Data quality issues: The model may also perform poorly if the data being analyzed is messy, incomplete or contains outliers.

5. Underfitting or overfitting: If the model is overly simplified or overly complex, it may not be able to effectively learn the patterns in the data, resulting in a low AUC and ROC score.

6. Multicollinearity: If there is a high degree of correlation between the independent variables, this can impact the model's ability to make accurate predictions.

7. Missing variables: If important variables are missing from the model, this can impact its ability to accurately predict the outcome.

It is important to investigate the reasons behind the low AUC and ROC score, and take the necessary steps to address them in order to improve the performance of the model.